Proactive Approach to Identify and Mitigate Cybersecurity Threats in Smart Building using AI

Smart buildings form a cornerstone of modern urban development. The integration of Internet of Things (IoT) devices in buildings has revolutionized facility management, improving system intelligence and user experience. However, these smart devices also introduce cybersecurity challenges and risks. The initiative "Identifying and Mitigating Cybersecurity Threats in Smart Building using AI" focuses on leveraging advanced artificial intelligence (AI) to address and mitigate cybersecurity threats within smart buildings. This study integrates predictive analytics, deep learning, and machine learning to identify and prevent cyberattacks, protecting sensitive data and networked IoT devices. By analyzing previous attack patterns, network behaviors, and potential vulnerabilities, this research aims to develop proactive security mechanisms that enhance threat detection and automate incident response. The main objective is to strengthen smart building security frameworks against the growing cyber threat landscape, improving system resilience and safeguarding critical data, including biometric information, image and voice recordings, and surveillance footage. This study will utilize the TON_IoT dataset and deploy AI models to evaluate the effectiveness of AI-driven cybersecurity solutions in real-world smart building environments. The expected outcomes include a robust intrusion detection system, improved threat detection accuracy, and enhanced cybersecurity strategies tailored for smart infrastructure.